Advertisement

The Algorithm of Automatic Text Summarization Based on Network Representation Learning

  • Xinghao Song
  • Chunming YangEmail author
  • Hui Zhang
  • Xujian Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11109)

Abstract

The graph models are an important method in automatic text summarization. However, there will be problems of vector sparseness and information redundancy in text map to graph. In this paper, we propose a graph clustering summarization algorithm based on network representation learning. The sentences graph was construed by TF-IDF, and controlled the number of edges by a threshold. The Node2Vec is used to embedding the graph, and the sentences were clustered by k-means. Finally, the Modularity is used to control the number of clusters, and generating a brief summary of the document. The experiments on the MultiLing 2013 show the proposed algorithm improves the F-Score in ROUGE-1 and ROUGE-2.

Keywords

Text summarization Network representation learning Graph clustering Modularity 

Notes

Acknowledgement

This work is supported by the Ministry of education of Humanities and Social Science project (17YJCZH260), the Next Generation Internet Technology Innovation Project (NGII20170901), the Fund of Fundamental Sichuan Civil-military Integration (JMRHH01, 18sxb017, 18sxb028).

References

  1. 1.
    Luhn, H.P.: The automatic creation of literature abstracts. IBM Corp. (1958)Google Scholar
  2. 2.
    Li, P., Lam, W., Bing, L., et al.: Deep Recurrent Generative Decoder for Abstractive Text Summarization. arXiv preprint arXiv:1708.00625 (2017)
  3. 3.
    Mani, I., Bloedorn, E.: Multi-document summarization by graph search and matching. In: Proceedings of AAAI 1997, pp. 622–628 (1997)Google Scholar
  4. 4.
    Erkan, G., Radev, D.R.: LexRank: graph-based lexical centrality as salience in text summarization. J. Qiqihar Jr. Teach. Coll. 2011, 22 (2004)Google Scholar
  5. 5.
    Ferreira, R., Freitas, F., Cabral, L.D.S., et al.: A four dimension graph model for automatic text summarization. In: IEEE/WIC/ACM International Joint Conferences on Web Intelligence, pp. 389–396. IEEE (2013)Google Scholar
  6. 6.
    Ferreira, R., Lins, R.D., Freitas, F., et al.: A new sentence similarity method based on a three-layer sentence representation. In: ACM Symposium on Document Engineering, pp. 25–34. ACM (2014)Google Scholar
  7. 7.
    Giannakopoulos, G., Karkaletsis, V., Vouros, G.: Testing the use of N-gram graphs in summarization sub-tasks. In: Proceedings of Text Analysis Conference, TAC 2008, pp. 158–167 (2008)Google Scholar
  8. 8.
    Salton, G., Fox, E.A., Wu, H.: Extended boolean information retrieval. Cornell University (1982)Google Scholar
  9. 9.
    Mikolov, T., Chen, K., Corrado, G., et al.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  10. 10.
    Perozzi, B., Alrfou, R., Skiena, S.: DeepWalk: online learning of social representations, pp. 701–710 (2014)Google Scholar
  11. 11.
    Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: KDD, p. 855 (2016)Google Scholar
  12. 12.
    Tang, J., Qu, M., Wang, M., et al.: LINE: large-scale information network embedding, vol. 2, pp. 1067–1077 (2015)Google Scholar
  13. 13.
    Newman, M.E.: Fast algorithm for detecting community structure in networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 69(6 Pt 2), 066133 (2003)Google Scholar
  14. 14.
    Lin, C.: ROUGE: a package for automatic evaluation of summaries. In: ACL, pp. 74–81 (2004)Google Scholar
  15. 15.
    Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. UNT Scholarly Works, pp. 404–411 (2004)Google Scholar
  16. 16.
    Steinberger, J., Jezek, K.: Using latent semantic analysis in text summarization and summary evaluation. In: International Conference ISIM, pp. 93–100 (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xinghao Song
    • 1
  • Chunming Yang
    • 1
    • 3
    Email author
  • Hui Zhang
    • 2
  • Xujian Zhao
    • 1
  1. 1.School of Computer Science and TechnologySouthwest University of Science and TechnologyMianyangChina
  2. 2.School of ScienceSouthwest University of Science and TechnologyMianyangChina
  3. 3.Sichuan Civil-Military Integration InstituteMianyangChina

Personalised recommendations